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GAR15 Global Exposure Dataset for Brunei

Identification Information
Citation
Originator
Originator
Publication Date
20151231
Title
GAR15 Global Exposure Dataset for Brunei
Geospatial Data Presentation Form
vector digital data
Collection Title
GAR15 Global Exposure Database
Publication Information
Publication Place
Publisher
United Nations. Office for Disaster Risk Reduction
Other Citation Details
Data retrieved from https://data.humdata.org/ on June 21, 2018.
Online Linkage
http://purl.stanford.edu/bb814dn0658
Abstract
This point shapefile includes estimation on the economic value of the exposed assets in Brunei as well as their physical characteristics in urban and rural agglomerations including estimation of population too. This information is key to assess the potential damages from different hazards to each of the exposed elements. The global exposure database is developed at 1km spatial resolution at coastal areas and at 5km spatial resolution everywhere else on the globe. It includes economic value, number of residents, and construction type of residential, commercial and industrial buildings, as well as hospitals and schools. Accessing national census has proved to be quite challenging. For estimating the non- residential distributions, especially for the countries for which no relevant published census data were available, several other sources such as World Housing Encyclopedia as well as expert judgment are used to make assumptions necessary to estimate the properties of the building stock. Combining all the components mentioned above, the economic value of each building class in one cell is assessed based on the disaggregation of the (national) Produced Capital at grid level. This downscaling was done by using the sub-national values of economic activity as a proxy. The result is the global distribution of the economic value of the urban and rural produced capital by construction class. Further details on the GAR Global Exposure Dataset can be found in technical background papers (De Bono, et.al, 2015), (Tolis et al., 2013) and (Pesaresi, et.al, 2015)..
Purpose
This dataset was generated using other global datasets; it should not be used for local applications (such as land use planning). The main purpose of GAR 2015 datasets is to broadly identify high risk areas at global level and for identification of areas where more detailed data should be collected. Some areas may be underestimated or overestimated. Given this analysis was conducted using global datasets, the resolution of which is not sufficient for in-situ planning, it should not be used for critical (like life saving) decisions. UNISDR and collaborators should in no case be liable for misuse or misinterpretation of the presented results. The designations employed and the presentation of material on the maps do not imply the expression of any opinion whatsoever on the part of UNISDR or the Secretariat of the United Nations concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries.
Supplemental Information
Main components of the global exposure dataset: Reference Grid The so 5x5km reference grid for GAR global exposure dataset includes the whole earth land surface, comprising uninhabited land areas. In this way the reference Grid will be able to handle eventually future data on crops pastures and forest areas. The total number of cells of the grid is 9,008,829. Inhabited cells correspond to 4,574,010. The 5x5km grid size was the choice balancing three criteria of (a) satisfactory size to capture effects for large scale hazards such as earthquake and cyclones at global scale, (b) consistency with the openly available socio-economic datasets with national or global sources, (c) optimizing the computation time Another grid at 30” resolution (around 1x1 km at equator) was set in order to hold exposure data related to coastal areas. The grid was only built for a sector including the first 10 km of coast worldwide. Boundaries of built-up environment (using BUREF) The next task is to define the boundaries of human settlements or building stock on the global and identified as urban, sub-urban, or rural. The boundaries of building stock is defined using satellite-imagery of land cover. The Global Built-up Reference Layer (BUREF2010) generated by JRC is a spatial raster dataset containing an estimation of the distribution and density of built-up areas (Pesaresi et al., 2015). It uses publicly available satellite-derived land cover information and per grid population density data to define the percentage of land occupied by buildings per each grid. Defining the “content” of each grid in exposure dataset using combination of various datasets: Population distribution The primary source of global exposure information is the distribution of people on the earth surface. A gridded population dataset is based on a regular grid, where each cell indicates the number of people living on it. In GEG-2015 development, the new LandScan data published on June 2012 by Oak Ridge National Laboratory was used and refer to the population as of July 2011 at 30” resolution (approx. 1 km equator). Night time light intensities or Visible Infrared Imaging Radiometer Suite (VIIRS) The intensities of nighttime lights represents a good proxy of human activities and they were already used at global scale to map economic activity. (Gosh, T. et al., 2010) Produced capital stock The economic value of buildings (capital stock) per country is estimated using a dataset for 152 countries from The World Bank (World Bank, 2011) has provides broad estimates of the current (2005) capital stock of machinery and structures, based on the Perpetual Inventory Method (PIM) and historical Gross Capital Formation (GCF) data. Furthermore, the World Bank scale‐up this estimate by 24% to account for the value of Urban Land. Gross regional product A raster of Gross Regional Product (GRP) distribution is generated by collecting and assembling all available information for 71 major countries using the following sources: Eurostat: 25 countries Beijing Normal University: 1 country (China) OECD: 1 country World Bank DECRG: 44 countries The GRP will be further integrated with the outputs from night time light intensities in order to generate a new indicator showing the GDP variation between national and subnational scales. These regional variations of economic activity within a country are used as the basis for geographical distribution of capital stock. Socio-economic indicators Socio economic indicators are used as proxies to estimate the use of the building stock for various sectors of commercial, industrial, public, education and health and various economic level for residential sector. Defining construction classes and distribution Once the density, values, and sectorial distribution of building stock in each cell are defined, the next step is to define the construction classes and the distribution of various construction classes in each grid. The World Agency of Planetary Monitoring Earthquake Risk Reduction (WAPMERR) gathered data on the sub-national distribution of building types for 18 countries using household data from national census as proxies. Countries selected include the largest heterogeneous ones (China, India and Indonesia) and represent 3.6 billion people, about 50% of the total population of the world. Data on characteristics of houses or households are given for residential/nonresidential groups and mainly divided in large urban small urban and rural areas classification. WAPMER developed the dataset for all countries using construction types defined by PAGER, a program of USGS.
Temporal Extent
Currentness Reference
ground condition
Time Instant
20151231
Bounding Box
West
114.104167
East
115.312500
North
5.054167
South
4.054167
Theme Keyword
Emergency management
Education
Population
Housing
Employment
Risk assessment
Theme Keyword Thesaurus
lcsh
Theme Keyword
society
economy
health
Theme Keyword Thesaurus
ISO 19115 Topic Categories
Place Keyword
Brunei
Place Keyword Thesaurus
geonames
Temporal Keyword
Access Restrictions
GAR 2015 datasets are available for free, for non-commercial purposes to governments, international organisations, universities, non-governmental organisations, the private sector and civil society according to this terms and conditions and the following disclaimers. This data can be downloaded and used for scientific and non-for-profit purposes without any specific permission. It is requested that these users cite the references accordingly in their publications. We would, however, appreciate if users of this data let us know how it was used and to receive a copy of or link to any related publication in order to better identify the needs of our users. For commercial applications please contact UNISDR.
Use Restrictions
This dataset was generated using other global datasets; it should not be used for local applications (such as land use planning). The main purpose of GAR 2015 datasets is to broadly identify high risk areas at global level and for identification of areas where more detailed data should be collected. Some areas may be underestimated or overestimated. Given this analysis was conducted using global datasets, the resolution of which is not sufficient for in-situ planning, it should not be used for critical (like life saving) decisions. UNISDR and collaborators should in no case be liable for misuse or misinterpretation of the presented results. The designations employed and the presentation of material on the maps do not imply the expression of any opinion whatsoever on the part of UNISDR or the Secretariat of the United Nations concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries.
Status
Complete
Maintenance and Update Frequency
None planned
Point of Contact
Contact Organization
United Nations. Office for Disaster Risk Reduction
City
Geneva
Country
CH
Contact Electronic Mail Address
isdr@un.org
Credit
United Nations Office for Disaster Risk Reduction and Global Resource Information Database. (2015). GAR15 Global Exposure Dataset for Brunei.United Nations Office for Disaster Risk Reduction. Availabile at: http://purl.stanford.edu/bb814dn0658
Native Data Set Environment
Version 6.2 (Build 9200) ; Esri ArcGIS 10.4.1.5686
Collection
Title
GAR15 Global Exposure Database
Spatial Data Organization Information
Direct Spatial Reference Method
Vector
Point and Vector Object Information
SDTS Terms Description
SDTS Point and Vector Object Type
Entity point
Point and Vector Object Count
215
Entity and Attribute Information
Entity Type
Entity Type Label
gar_exp_BRN
Attributes
FID
Internal feature number. (Sequential unique whole numbers that are automatically generated.)
Definition Source
Esri
Shape
Feature geometry. (Coordinates defining the features.)
Definition Source
Esri
id_5x
iso3
ISO 3 letter code
bed_prv_pu
bed_pub_pu
Health-public sector-urban population
edu_prv_pu
Education-private sector-urban population
edu_pub_pu
Education-public sector-urban population
emp_agr_pu
Employment-agricol sector-urban population
emp_gov_pu
Employment-government sector-urban population
emp_ind_pu
Employment-industrial sector-urban population
emp_ser_pu
Employment-service sector-urban population
ic_high_pu
Housing-high income group-urban population
ic_low_pu
Housing-low income group-urban population
ic_mhg_pu
Housing-upper middle income group-urban population
ic_mlw_pu
Housing-lower middle income group-urban population
tot_pu
Total public sector
bed_prv_cu
Health-private sector-capital stock urban (built environment) in million USD $
bed_pub_cu
Health-public sector-capital stock urban (built environment) in million USD $
edu_prv_cu
Education-private sector-capital stock urban (built environment) in million USD $
edu_pub_cu
Education-public sector-capital stock urban (built environment) in million USD $
emp_agr_cu
Employment-agricol sector-capital stock urban (built environment) in million USD $
emp_gov_cu
Employment-government sector-capital stock urban (built environment) in million USD $
emp_ind_cu
Employment-industrial sector-capital stock urban (built environment) in million USD $
emp_ser_cu
Employment-service sector-capital stock urban (built environment) in million USD $
ic_high_cu
Housing-high income group-capital stock urban (built environment) in million USD $
ic_low_cu
Housing-low income group-capital stock urban (built environment) in million USD $
ic_mhg_cu
Housing-upper middle income group-capital stock urban (built environment) in million USD $
ic_mlw_cu
Housing-lower middle income group-capital stock urban (built environment) in million USD $
tot_cu
Total capital stock urban (built environment) in million USD $
bed_prv_pr
Health-private sector-rural population
bed_pub_pr
Health-public sector-rural population
edu_prv_pr
Education-private sector-rural population
edu_pub_pr
Education-public sector-rural population
emp_agr_pr
Employment-agricol sector-rural population
emp_gov_pr
Employment-government sector-rural population
emp_ind_pr
Employment-industrial sector-rural population
emp_ser_pr
Employment-service sector-rural population
ic_high_pr
Housing-high income group-rural population
ic_low_pr
Housing-low income group-rural population
ic_mhg_pr
Housing-upper middle income group-rural population
ic_mlw_pr
Housing-lower middle income group-rural population
tot_pr
Total rural population
bed_prv_cr
bed_pub_cr
Health-public sector-capital stock rural (built environment) in million USD $
edu_prv_cr
Education-private sector-capital stock rural (built environment) in million USD $
edu_pub_cr
Education-public sector-capital stock rural (built environment) in million USD $
emp_agr_cr
Employment-agricol sector-capital stock rural (built environment) in million USD $
emp_gov_cr
Employment-government sector-capital stock rural (built environment) in million USD $
emp_ind_cr
Employment-industrial sector-capital stock rural (built environment) in million USD $
emp_ser_cr
Employment-service sector-capital stock rural (built environment) in million USD $
ic_high_cr
Housing-high income group-capital stock rural (built environment) in million USD $
ic_low_cr
Housing-low income group-capital stock rural (built environment) in million USD $
ic_mhg_cr
Housing-upper middle income group-capital stock rural (built environment) in million USD $
ic_mlw_cr
Housing-lower middle income group-capital stock rural (built environment) in million USD $
tot_cr
Total capital stock rural (built environment) in million USD $
tot_pob
Total population
tot_val
Total value
Distribution Information
Distributor
Stanford Geospatial Center
Name
Metadata Reference Information
Metadata Date
20180626
Metadata Contact
Contact Information
Contact Organization Primary
Contact Organization
Stanford Geospatial Center
Contact Address
Address
Branner Earth Sciences Library
Address
Mitchell Building, 2nd Floor
Address
397 Panama Mall
City
Stanford
State or Province
California
Postal Code
94305
Country
US
Contact Voice Telephone
650-723-2746
Contact Electronic Mail Address
brannerlibrary@stanford.edu
Metadata Standard Name
FGDC Content Standard for Digital Geospatial Metadata
Metadata Standard Version
FGDC-STD-001-1998

GAR15 Global Exposure Dataset for Brunei

Identification Information

Citation
Title
GAR15 Global Exposure Dataset for Brunei
Originator
Global Resource Information Database
Originator
United Nations. Office for Disaster Risk Reduction
Publisher
United Nations. Office for Disaster Risk Reduction
Place of Publication
Geneva , CH
Publication Date
2015-12-31
Identifier
http://purl.stanford.edu/bb814dn0658
Geospatial Data Presentation Form
mapDigital
Collection Title
GAR15 Global Exposure Database
Other Citation Details
Data retrieved from https://data.humdata.org/ on June 21, 2018.
Abstract
This point shapefile includes estimation on the economic value of the exposed assets in Brunei as well as their physical characteristics in urban and rural agglomerations including estimation of population too. This information is key to assess the potential damages from different hazards to each of the exposed elements. The global exposure database is developed at 1km spatial resolution at coastal areas and at 5km spatial resolution everywhere else on the globe. It includes economic value, number of residents, and construction type of residential, commercial and industrial buildings, as well as hospitals and schools. Accessing national census has proved to be quite challenging. For estimating the non- residential distributions, especially for the countries for which no relevant published census data were available, several other sources such as World Housing Encyclopedia as well as expert judgment are used to make assumptions necessary to estimate the properties of the building stock. Combining all the components mentioned above, the economic value of each building class in one cell is assessed based on the disaggregation of the (national) Produced Capital at grid level. This downscaling was done by using the sub-national values of economic activity as a proxy. The result is the global distribution of the economic value of the urban and rural produced capital by construction class. Further details on the GAR Global Exposure Dataset can be found in technical background papers (De Bono, et.al, 2015), (Tolis et al., 2013) and (Pesaresi, et.al, 2015)..
Purpose
This dataset was generated using other global datasets; it should not be used for local applications (such as land use planning). The main purpose of GAR 2015 datasets is to broadly identify high risk areas at global level and for identification of areas where more detailed data should be collected. Some areas may be underestimated or overestimated. Given this analysis was conducted using global datasets, the resolution of which is not sufficient for in-situ planning, it should not be used for critical (like life saving) decisions. UNISDR and collaborators should in no case be liable for misuse or misinterpretation of the presented results. The designations employed and the presentation of material on the maps do not imply the expression of any opinion whatsoever on the part of UNISDR or the Secretariat of the United Nations concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries.
Supplemental Information
Main components of the global exposure dataset: Reference Grid The so 5x5km reference grid for GAR global exposure dataset includes the whole earth land surface, comprising uninhabited land areas. In this way the reference Grid will be able to handle eventually future data on crops pastures and forest areas. The total number of cells of the grid is 9,008,829. Inhabited cells correspond to 4,574,010. The 5x5km grid size was the choice balancing three criteria of (a) satisfactory size to capture effects for large scale hazards such as earthquake and cyclones at global scale, (b) consistency with the openly available socio-economic datasets with national or global sources, (c) optimizing the computation time Another grid at 30” resolution (around 1x1 km at equator) was set in order to hold exposure data related to coastal areas. The grid was only built for a sector including the first 10 km of coast worldwide. Boundaries of built-up environment (using BUREF) The next task is to define the boundaries of human settlements or building stock on the global and identified as urban, sub-urban, or rural. The boundaries of building stock is defined using satellite-imagery of land cover. The Global Built-up Reference Layer (BUREF2010) generated by JRC is a spatial raster dataset containing an estimation of the distribution and density of built-up areas (Pesaresi et al., 2015). It uses publicly available satellite-derived land cover information and per grid population density data to define the percentage of land occupied by buildings per each grid. Defining the “content” of each grid in exposure dataset using combination of various datasets: Population distribution The primary source of global exposure information is the distribution of people on the earth surface. A gridded population dataset is based on a regular grid, where each cell indicates the number of people living on it. In GEG-2015 development, the new LandScan data published on June 2012 by Oak Ridge National Laboratory was used and refer to the population as of July 2011 at 30” resolution (approx. 1 km equator). Night time light intensities or Visible Infrared Imaging Radiometer Suite (VIIRS) The intensities of nighttime lights represents a good proxy of human activities and they were already used at global scale to map economic activity. (Gosh, T. et al., 2010) Produced capital stock The economic value of buildings (capital stock) per country is estimated using a dataset for 152 countries from The World Bank (World Bank, 2011) has provides broad estimates of the current (2005) capital stock of machinery and structures, based on the Perpetual Inventory Method (PIM) and historical Gross Capital Formation (GCF) data. Furthermore, the World Bank scale‐up this estimate by 24% to account for the value of Urban Land. Gross regional product A raster of Gross Regional Product (GRP) distribution is generated by collecting and assembling all available information for 71 major countries using the following sources: Eurostat: 25 countries Beijing Normal University: 1 country (China) OECD: 1 country World Bank DECRG: 44 countries The GRP will be further integrated with the outputs from night time light intensities in order to generate a new indicator showing the GDP variation between national and subnational scales. These regional variations of economic activity within a country are used as the basis for geographical distribution of capital stock. Socio-economic indicators Socio economic indicators are used as proxies to estimate the use of the building stock for various sectors of commercial, industrial, public, education and health and various economic level for residential sector. Defining construction classes and distribution Once the density, values, and sectorial distribution of building stock in each cell are defined, the next step is to define the construction classes and the distribution of various construction classes in each grid. The World Agency of Planetary Monitoring Earthquake Risk Reduction (WAPMERR) gathered data on the sub-national distribution of building types for 18 countries using household data from national census as proxies. Countries selected include the largest heterogeneous ones (China, India and Indonesia) and represent 3.6 billion people, about 50% of the total population of the world. Data on characteristics of houses or households are given for residential/nonresidential groups and mainly divided in large urban small urban and rural areas classification. WAPMER developed the dataset for all countries using construction types defined by PAGER, a program of USGS.
Temporal Extent
Currentness Reference
ground condition
Time Instant
2015-12-31T00:00:00
Bounding Box
West
114.104167
East
115.3125
North
5.054167
South
4.054167
ISO Topic Category
society
economy
health
Place Keyword
Brunei
Place Keyword Thesaurus
geonames
Theme Keyword
Emergency management
Education
Population
Housing
Employment
Risk assessment
Theme Keyword Thesaurus
lcsh
Resource Constraints
Use Limitation
This dataset was generated using other global datasets; it should not be used for local applications (such as land use planning). The main purpose of GAR 2015 datasets is to broadly identify high risk areas at global level and for identification of areas where more detailed data should be collected. Some areas may be underestimated or overestimated. Given this analysis was conducted using global datasets, the resolution of which is not sufficient for in-situ planning, it should not be used for critical (like life saving) decisions. UNISDR and collaborators should in no case be liable for misuse or misinterpretation of the presented results. The designations employed and the presentation of material on the maps do not imply the expression of any opinion whatsoever on the part of UNISDR or the Secretariat of the United Nations concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries.
Legal Constraints
Use Restrictions
otherRestrictions
Other Restrictions
GAR 2015 datasets are available for free, for non-commercial purposes to governments, international organisations, universities, non-governmental organisations, the private sector and civil society according to this terms and conditions and the following disclaimers. This data can be downloaded and used for scientific and non-for-profit purposes without any specific permission. It is requested that these users cite the references accordingly in their publications. We would, however, appreciate if users of this data let us know how it was used and to receive a copy of or link to any related publication in order to better identify the needs of our users. For commercial applications please contact UNISDR.
Status
completed
Maintenance and Update Frequency
notPlanned
Collection
Collection Title
GAR15 Global Exposure Database
URL
https://purl.stanford.edu/fs274ns2204
Language
eng
Credit
United Nations Office for Disaster Risk Reduction and Global Resource Information Database. (2015). GAR15 Global Exposure Dataset for Brunei.United Nations Office for Disaster Risk Reduction. Availabile at: http://purl.stanford.edu/bb814dn0658
Point of Contact
Contact
United Nations. Office for Disaster Risk Reduction
City
Geneva
Country
CH
Email
isdr@un.org

Spatial Reference Information

Reference System Identifier
Code
4326
Code Space
EPSG
Version
6.14(3.0.1)

Distribution Information

Format Name
Shapefile
Distributor
Stanford Geospatial Center
Online Access
http://purl.stanford.edu/bb814dn0658
Protocol
http
Name
gar_exp_BRN.shp

Content Information

Feature Catalog Description
Compliance Code
false
Language
eng
Included With Dataset
true
Feature Catalog Citation
Title
Entity and Attribute Information
Feature Catalog Identifier
c2d836cd-a151-4470-8363-0600cb97685a

Spatial Representation Information

Vector
Topology Level
geometryOnly
Vector Object Type
point
Vector Object Count
215

Metadata Reference Information

Hierarchy Level
dataset
Metadata File Identifier
edu.stanford.purl:bb814dn0658
Parent Identifier
https://purl.stanford.edu/fs274ns2204.mods
Dataset URI
http://purl.stanford.edu/bb814dn0658
Metadata Date Stamp
2018-06-26
Metadata Standard Name
ISO 19139 Geographic Information - Metadata - Implementation Specification
Metadata Standard Version
2007
Character Set
utf8
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